{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DNA_SIZE = 10           # DNA的长度\n",
    "POP_SIZE = 100          # 初始种群大小\n",
    "CROSS_RATE = 0.8        # DNA交叉概率\n",
    "MUTATION_RATE = 0.003   # DNA变异概率\n",
    "N_GENERATIONS = 200     # 迭代次数\n",
    "X_BOUND = [0, 5]        # 迭代中x的取值区间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里是为了求$f(x)=sin(10x)*x+cos(2x)*x$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def F(x):\n",
    "    # 目标函数\n",
    "    return np.sin(10*x)*x+np.cos(2*x)*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fitness(pred):\n",
    "    # 获得适应度，与min比较，越大则适应度越大，便于我们寻找最大值\n",
    "    return pred-np.min(pred)+1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def translateDNA(pop):\n",
    "    # 将DNA的二进制序列映射到x范围内\n",
    "    return pop.dot(2**np.arange(DNA_SIZE)[::-1])/float(2**DNA_SIZE-1)*(X_BOUND[1]-X_BOUND[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "2.517106549364614\n5.0\n"
    }
   ],
   "source": [
    "# 测试translateDNA\n",
    "test_data = np.array([1,0,0,0,0,0,0,0,1,1])\n",
    "print(translateDNA(test_data))\n",
    "# 测试translateDNA\n",
    "test_data = np.array([1,1,1,1,1,1,1,1,1,1])\n",
    "print(translateDNA(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def select(pop, fitness):\n",
    "    # 根据适应度fitness来选择pop中的parent，fitness越大越有可能选到\n",
    "    index = np.random.choice(np.arange(POP_SIZE), size=POP_SIZE,replace=True,p=fitness/fitness.sum())\n",
    "\n",
    "    return pop[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def crossover(parent,pop):\n",
    "    # 交叉函数\n",
    "    if np.random.rand() < CROSS_RATE:\n",
    "        i_ = np.random.randint(0,POP_SIZE,size=1) # 随机选择一个母本的index\n",
    "        print('i_:',i_)\n",
    "        print('i_.size',i_.size)\n",
    "        cross_points = np.random.randint(0,2,size = DNA_SIZE).astype(np.bool)   # 选择交叉的点\n",
    "        print('cross_point',cross_points)\n",
    "        print('corssize:',cross_points.size)\n",
    "        print(parent[cross_points])\n",
    "        print(pop[i_,cross_points])\n",
    "\n",
    "        # 交配并产生子代\n",
    "        parent[cross_points] = pop[i_,cross_points]\n",
    "        print(parent[cross_points])\n",
    "    \n",
    "    return parent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def muate(child):\n",
    "    # 变异函数\n",
    "    for point in range(DNA_SIZE):\n",
    "        if np.random.rand() < MUTATION_RATE:\n",
    "            child[point] = 1 if child[point] == 0 else 0\n",
    "    return child"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 初始化DNA为pop\n",
    "pop = np.random.randint(2, size=(POP_SIZE, DNA_SIZE))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 测试pop的值\n",
    "# print(pop)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": " [88]\ni_.size 1\ncross_point [ True False False False  True False False False  True False]\ncorssize: 10\n[1 1 1]\n[1 1 1]\n[1 1 1]\ni_: [63]\ni_.size 1\ncross_point [ True False  True  True False  True False  True False  True]\ncorssize: 10\n[1 1 0 0 0 0]\n[1 1 0 0 0 0]\n[1 1 0 0 0 0]\ni_: [88]\ni_.size 1\ncross_point [ True False False  True  True False False  True  True False]\ncorssize: 10\n[1 0 1 0 1]\n[1 0 1 0 1]\n[1 0 1 0 1]\ni_: [34]\ni_.size 1\ncross_point [False False False  True  True  True False  True  True  True]\ncorssize: 10\n[0 1 0 0 1 0]\n[0 1 0 0 1 0]\n[0 1 0 0 1 0]\ni_: [31]\ni_.size 1\ncross_point [False False  True False False False  True False  True False]\ncorssize: 10\n[1 0 1]\n[1 0 1]\n[1 0 1]\ni_: [90]\ni_.size 1\ncross_point [False False  True False False  True False  True False False]\ncorssize: 10\n[1 0 0]\n[1 0 0]\n[1 0 0]\ni_: [36]\ni_.size 1\ncross_point [ True False False False False False False  True  True False]\ncorssize: 10\n[1 0 1]\n[1 0 1]\n[1 0 1]\ni_: [42]\ni_.size 1\ncross_point [ True  True False False  True  True  True  True False False]\ncorssize: 10\n[1 0 1 0 0 0]\n[1 0 1 0 0 0]\n[1 0 1 0 0 0]\ni_: [95]\ni_.size 1\ncross_point [False False False  True False  True  True False  True  True]\ncorssize: 10\n[0 0 0 1 0]\n[0 0 0 1 0]\n[0 0 0 1 0]\ni_: [72]\ni_.size 1\ncross_point [False False False False  True  True False  True  True False]\ncorssize: 10\n[1 0 0 1]\n[1 0 0 1]\n[1 0 0 1]\ni_: [34]\ni_.size 1\ncross_point [ True  True False  True False False  True False  True  True]\ncorssize: 10\n[1 0 0 0 1 0]\n[1 0 0 0 1 0]\n[1 0 0 0 1 0]\ni_: [71]\ni_.size 1\ncross_point [ True False False  True  True  True  True  True False  True]\ncorssize: 10\n[1 0 1 0 0 0 0]\n[1 0 1 0 0 0 0]\n[1 0 1 0 0 0 0]\ni_: [47]\ni_.size 1\ncross_point [ True False  True  True False False False  True False False]\ncorssize: 10\n[1 1 0 0]\n[1 1 0 0]\n[1 1 0 0]\ni_: [25]\ni_.size 1\ncross_point [ True  True False False  True  True  True False  True False]\ncorssize: 10\n[1 0 1 0 0 1]\n[1 0 1 0 0 1]\n[1 0 1 0 0 1]\ni_: [58]\ni_.size 1\ncross_point [ True False  True  True False  True  True  True  True False]\ncorssize: 10\n[1 1 0 0 0 0 1]\n[1 1 0 0 0 0 1]\n[1 1 0 0 0 0 1]\ni_: [61]\ni_.size 1\ncross_point [False  True False False  True  True False  True False  True]\ncorssize: 10\n[0 1 0 0 0]\n[0 1 0 0 0]\n[0 1 0 0 0]\ni_: [77]\ni_.size 1\ncross_point [False  True False  True False False False False  True False]\ncorssize: 10\n[0 0 1]\n[0 0 1]\n[0 0 1]\ni_: [93]\ni_.size 1\ncross_point [False  True  True  True  True  True False False False  True]\ncorssize: 10\n[0 1 0 1 0 0]\n[0 1 0 1 0 0]\n[0 1 0 1 0 0]\ni_: [36]\ni_.size 1\ncross_point [False  True False  True  True False False  True False  True]\ncorssize: 10\n[0 0 1 0 0]\n[0 0 1 0 0]\n[0 0 1 0 0]\ni_: [87]\ni_.size 1\ncross_point [False False False  True  True  True  True  True  True  True]\ncorssize: 10\n[0 1 0 0 0 1 0]\n[0 1 0 0 0 1 0]\n[0 1 0 0 0 1 0]\ni_: [74]\ni_.size 1\ncross_point [False  True False False False False  True False False False]\ncorssize: 10\n[0 0]\n[0 0]\n[0 0]\ni_: [28]\ni_.size 1\ncross_point [False False False  True False False  True False  True  True]\ncorssize: 10\n[0 0 1 0]\n[0 0 1 0]\n[0 0 1 0]\ni_: [39]\ni_.size 1\ncross_point [False  True False  True  True False  True False  True False]\ncorssize: 10\n[0 0 1 0 1]\n[0 0 1 0 1]\n[0 0 1 0 1]\ni_: [44]\ni_.size 1\ncross_point [ True  True  True  True  True  True  True False  True  True]\ncorssize: 10\n[1 0 1 0 1 0 0 1 0]\n[1 0 1 0 1 0 0 1 0]\n[1 0 1 0 1 0 0 1 0]\ni_: [23]\ni_.size 1\ncross_point [False False  True False  True  True False False False  True]\ncorssize: 10\n[1 1 0 0]\n[1 1 0 0]\n[1 1 0 0]\ni_: [40]\ni_.size 1\ncross_point [False False False False False  True False  True  True False]\ncorssize: 10\n[0 0 1]\n[0 0 1]\n[0 0 1]\ni_: [17]\ni_.size 1\ncross_point [ True  True False False False  True False False False False]\ncorssize: 10\n[1 0 0]\n[1 0 0]\n[1 0 0]\ni_: [34]\ni_.size 1\ncross_point [False  True False False  True False  True False  True  True]\ncorssize: 10\n[0 1 0 1 0]\n[0 1 0 1 0]\n[0 1 0 1 0]\ni_: [27]\ni_.size 1\ncross_point [ True  True  True  True False  True  True  True  True  True]\ncorssize: 10\n[1 0 1 0 0 0 0 1 0]\n[1 0 1 0 0 0 0 1 0]\n[1 0 1 0 0 0 0 1 0]\ni_: [28]\ni_.size 1\ncross_point [ True  True  True  True  True False  True False False False]\ncorssize: 10\n[1 0 1 0 1 0]\n[1 0 1 0 1 0]\n[1 0 1 0 1 0]\ni_: [22]\ni_.size 1\ncross_point [False  True False False  True False False  True  True  True]\ncorssize: 10\n[0 1 0 1 0]\n[0 1 0 1 0]\n[0 1 0 1 0]\ni_: [14]\ni_.size 1\ncross_point [False  True  True  True False False False  True False  True]\ncorssize: 10\n[0 1 0 0 0]\n[0 1 0 0 0]\n[0 1 0 0 0]\ni_: [1]\ni_.size 1\ncross_point [ True  True  True False False  True  True  True False False]\ncorssize: 10\n[1 0 1 0 0 0]\n[1 0 1 0 0 0]\n[1 0 1 0 0 0]\ni_: [50]\ni_.size 1\ncross_point [ True  True  True False False False  True False False  True]\ncorssize: 10\n[1 0 1 0 0]\n[1 0 1 0 0]\n[1 0 1 0 0]\ni_: [27]\ni_.size 1\ncross_point [ True  True  True False False  True  True  True  True False]\ncorssize: 10\n[1 0 1 0 0 0 1]\n[1 0 1 0 0 0 1]\n[1 0 1 0 0 0 1]\ni_: [35]\ni_.size 1\ncross_point [False False  True False False  True  True  True  True  True]\ncorssize: 10\n[1 0 0 0 1 0]\n[1 0 0 0 1 0]\n[1 0 0 0 1 0]\ni_: [75]\ni_.size 1\ncross_point [False False  True  True False False  True  True False False]\ncorssize: 10\n[1 0 0 0]\n[1 0 0 0]\n[1 0 0 0]\ni_: [62]\ni_.size 1\ncross_point [ True False False  True  True False  True  True  True  True]\ncorssize: 10\n[1 0 1 0 0 1 0]\n[1 0 1 0 0 1 0]\n[1 0 1 0 0 1 0]\ni_: [61]\ni_.size 1\ncross_point [ True  True  True  True  True  True  True False  True  True]\ncorssize: 10\n[1 0 1 0 1 0 0 1 0]\n[1 0 1 0 1 0 0 1 0]\n[1 0 1 0 1 0 0 1 0]\ni_: [47]\ni_.size 1\ncross_point [ True  True  True False False  True  True  True  True  True]\ncorssize: 10\n[1 0 1 0 0 0 1 0]\n[1 0 1 0 0 0 1 0]\n[1 0 1 0 0 0 1 0]\ni_: [74]\ni_.size 1\ncross_point [False  True False  True False False  True  True  True False]\ncorssize: 10\n[0 0 0 0 1]\n[0 0 0 0 1]\n[0 0 0 0 1]\ni_: [26]\ni_.size 1\ncross_point [ True  True False False False  True  True False False False]\ncorssize: 10\n[1 0 0 0]\n[1 0 0 0]\n[1 0 0 0]\ni_: [91]\ni_.size 1\ncross_point [ True False False False  True False  True  True  True  True]\ncorssize: 10\n[1 1 0 0 1 0]\n[1 1 0 0 1 0]\n[1 1 0 0 1 0]\ni_: [59]\ni_.size 1\ncross_point [ True  True False  True False False False False  True False]\ncorssize: 10\n[1 0 0 1]\n[1 0 0 1]\n[1 0 0 1]\ni_: [95]\ni_.size 1\ncross_point [False  True  True  True False  True  True  True  True False]\ncorssize: 10\n[0 1 0 0 0 0 1]\n[0 1 0 0 0 0 1]\n[0 1 0 0 0 0 1]\ni_: [45]\ni_.size 1\ncross_point [ True False False False  True False  True  True False  True]\ncorssize: 10\n[1 1 0 0 0]\n[1 1 0 0 0]\n[1 1 0 0 0]\ni_: [16]\ni_.size 1\ncross_point [ True  True  True  True False  True False  True False False]\ncorssize: 10\n[1 0 1 0 0 0]\n[1 0 1 0 0 0]\n[1 0 1 0 0 0]\ni_: [48]\ni_.size 1\ncross_point [False False  True  True  True  True  True  True False False]\ncorssize: 10\n[1 0 1 0 0 0]\n[1 0 1 0 0 0]\n[1 0 1 0 0 0]\ni_: [78]\ni_.size 1\ncross_point [ True  True False  True  True False  True  True False  True]\ncorssize: 10\n[1 0 0 1 0 0 0]\n[1 0 0 1 0 0 0]\n[1 0 0 1 0 0 0]\ni_: [89]\ni_.size 1\ncross_point [ True False False False  True  True False  True False  True]\ncorssize: 10\n[1 1 0 0 0]\n[1 1 0 0 0]\n[1 1 0 0 0]\ni_: [79]\ni_.size 1\ncross_point [False False  True  True False False False  True  True  True]\ncorssize: 10\n[1 0 0 1 0]\n[1 0 0 1 0]\n[1 0 0 1 0]\ni_: [40]\ni_.size 1\ncross_point [ True False False  True False False False  True False  True]\ncorssize: 10\n[1 0 0 0]\n[1 0 0 0]\n[1 0 0 0]\ni_: [44]\ni_.size 1\ncross_point [ True  True  True  True  True  True  True  True False  True]\ncorssize: 10\n[1 0 1 0 1 0 0 0 0]\n[1 0 1 0 1 0 0 0 0]\n[1 0 1 0 1 0 0 0 0]\ni_: [18]\ni_.size 1\ncross_point [ True  True False False False  True False False False  True]\ncorssize: 10\n[1 0 0 0]\n[1 0 0 0]\n[1 0 0 0]\ni_: [14]\ni_.size 1\ncross_point [False False  True  True False False False  True  True False]\ncorssize: 10\n[1 0 0 1]\n[1 0 0 1]\n[1 0 0 1]\ni_: [56]\ni_.size 1\ncross_point [False  True False  True False False  True False  True False]\ncorssize: 10\n[0 0 0 1]\n[0 0 0 1]\n[0 0 0 1]\ni_: [53]\ni_.size 1\ncross_point [ True  True False  True  True  True  True  True  True  True]\ncorssize: 10\n[1 0 0 1 0 0 0 1 0]\n[1 0 0 1 0 0 0 1 0]\n[1 0 0 1 0 0 0 1 0]\ni_: [19]\ni_.size 1\ncross_point [False False False False  True  True  True  True  True False]\ncorssize: 10\n[1 0 0 0 1]\n[1 0 0 0 1]\n[1 0 0 0 1]\ni_: [43]\ni_.size 1\ncross_point [False False False  True  True  True  True False False False]\ncorssize: 10\n[0 1 0 0]\n[0 1 0 0]\n[0 1 0 0]\nMost fitted DNA:  [1 0 1 0 1 0 0 0 1 0]\ni_: [7]\ni_.size 1\ncross_point [ True False False  True  True  True  True  True  True  True]\ncorssize: 10\n[1 0 1 0 0 0 1 0]\n[1 0 1 0 0 0 1 0]\n[1 0 1 0 0 0 1 0]\ni_: [18]\ni_.size 1\ncross_point [False False  True False  True  True False  True False  True]\ncorssize: 10\n[1 1 0 0 0]\n[1 1 0 0 0]\n[1 1 0 0 0]\ni_: [76]\ni_.size 1\ncross_point [False False False False  True  True False False False False]\ncorssize: 10\n[1 0]\n[1 0]\n[1 0]\ni_: [72]\ni_.size 1\ncross_point [False  True  True False False False False  True  True  True]\ncorssize: 10\n[0 1 0 1 0]\n[0 1 0 1 0]\n[0 1 0 1 0]\ni_: [90]\ni_.size 1\ncross_point [ True False False False  True  True  True False  True False]\ncorssize: 10\n[1 1 0 0 1]\n[1 1 0 0 1]\n[1 1 0 0 1]\ni_: [30]\ni_.size 1\ncross_point [False False  True  True  True  True  True False  True  True]\ncorssize: 10\n[1 0 1 0 0 1 0]\n[1 0 1 0 0 1 0]\n[1 0 1 0 0 1 0]\ni_: [32]\ni_.size 1\ncross_point [False  True  True False False  True False False False False]\ncorssize: 10\n[0 1 0]\n[0 1 0]\n[0 1 0]\ni_: [46]\ni_.size 1\ncross_point [ True False False  True False False  True  True  True False]\ncorssize: 10\n[1 0 0 0 1]\n[1 0 0 0 1]\n[1 0 0 0 1]\ni_: [69]\ni_.size 1\ncross_point [False False False False False  True  True  True False  True]\ncorssize: 10\n[0 0 0 0]\n[0 0 0 0]\n[0 0 0 0]\ni_: [89]\ni_.size 1\ncross_point [ True  True  True False  True False False  True  True False]\ncorssize: 10\n[1 0 1 1 0 1]\n[1 0 1 1 0 1]\n[1 0 1 1 0 1]\ni_: [43]\ni_.size 1\ncross_point [ True False  True  True False False  True  True  True False]\ncorssize: 10\n[1 1 0 0 0 1]\n[1 1 0 0 0 1]\n[1 1 0 0 0 1]\ni_: [22]\ni_.size 1\ncross_point [False False  True False  True  True False False False  True]\ncorssize: 10\n[1 1 0 0]\n[1 1 0 0]\n[1 1 0 0]\ni_: [98]\ni_.size 1\ncross_point [False  True False False  True  True  True False False False]\ncorssize: 10\n[0 1 0 0]\n[0 1 0 0]\n[0 1 0 0]\ni_: [77]\ni_.size 1\ncross_point [ True False  True False False  True  True  True False False]\ncorssize: 10\n[1 1 0 0 0]\n[1 1 0 0 0]\n[1 1 0 0 0]\ni_: [37]\ni_.size 1\ncross_point [ True  True False False False False  True  True  True False]\ncorssize: 10\n[1 0 0 0 1]\n[1 0 0 0 1]\n[1 0 0 0 1]\ni_: [34]\ni_.size 1\ncross_point [False False False False  True  True  True False  True  True]\ncorssize: 10\n[1 0 0 1 0]\n[1 0 0 1 0]\n[1 0 0 1 0]\ni_: [6]\ni_.size 1\ncross_point [ True  True False  True  True False  True False  True  True]\ncorssize: 10\n[1 0 0 1 0 1 0]\n[1 0 0 1 0 1 0]\n[1 0 0 1 0 1 0]\ni_: [28]\ni_.size 1\ncross_point [False  True  True  True  True  True  True False  True  True]\ncorssize: 10\n[0 1 0 1 0 0 1 0]\n[0 1 0 1 0 0 1 0]\n[0 1 0 1 0 0 1 0]\ni_: [59]\ni_.size 1\ncross_point [ True False  True  True False False False False  True  True]\ncorssize: 10\n[1 1 0 1 0]\n[1 1 0 1 0]\n[1 1 0 1 0]\ni_: [89]\ni_.size 1\ncross_point [False  True  True False False  True  True  True  True False]\ncorssize: 10\n[0 1 0 0 0 1]\n[0 1 0 0 0 1]\n[0 1 0 0 0 1]\ni_: 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    }
   ],
   "source": [
    "for _ in range(N_GENERATIONS):\n",
    "    # 循环进行遗传函数的迭代\n",
    "    \n",
    "    F_values = F(translateDNA(pop)) # 通过提取DNA计算\n",
    "\n",
    "    # 中间过程的绘图\n",
    "    # if 'sca' in globals():\n",
    "    #     sca.remove()\n",
    "    # sca = plt.scatter(translateDNA(pop), F_values,\n",
    "    #                   s=200, lw=0, c='red', alpha=0.5)\n",
    "    # x = np.linspace(*X_BOUND,200)\n",
    "    # plt.plot(x,F(x))\n",
    "    # plt.pause(0.05)\n",
    "    # 中间过程的绘图结束\n",
    "\n",
    "    # GA 遗传变异迭代一次\n",
    "    fitness = get_fitness(F_values)\n",
    "    print(\"Most fitted DNA: \", pop[np.argmax(fitness), :])\n",
    "    pop = select(pop,fitness)\n",
    "    pop_copy = pop.copy()\n",
    "    for parent in pop:\n",
    "        child = crossover(parent,pop_copy)\n",
    "        child = muate(child)\n",
    "        parent[:]=child        # 用子代替换父代\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
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     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 最终结果绘制\n",
    "plt.ion()                           # 打开交互模式\n",
    "x = np.linspace(*X_BOUND,200)\n",
    "plt.plot(x,F(x))\n",
    "\n",
    "plt.scatter(translateDNA(pop),F_values,s=200,lw=0,c='red',alpha=0.5)\n",
    "print(\"x:\",translateDNA(pop))\n",
    "print(type(translateDNA(pop)))\n",
    "print(len(translateDNA(pop)))\n",
    "print(\"max:\", F_values)\n",
    "plt.ioff()                          # 关闭交互模式\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.4 64-bit",
   "language": "python",
   "name": "python38464bit1416437edcaf49af8e39476b27d510d7"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.4-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}